Splice site recognition with Neural Networks

The following preprint is available in postscript form by anonymous ftp
"Prediction of human mRNA donor and acceptor sites from the DNA
sequence". S. Brunak, J. Engelbrecht and S. Knudsen.
Journal of Molecular Biology, to appear.
Abstract:
Artificial neural networks have been applied to the prediction of
splice site location in human pre-mRNA. A joint prediction scheme where
prediction of transition regions between introns and exons regulates a
cutoff level for splice site assignment was able to predict splice site
locations with confidence levels far better than previously reported in
the literature. The problem of predicting donor and acceptor sites in
human genes is hampered by the presence of numerous amounts of false
positives --- in the paper the distribution of these false splice sites
is examined and linked to a possible scenario for the splicing
mechanism in vivo. When the presented method detects 95% of the true
donor and acceptor sites it makes less than 0.1% false donor site
assignments and less than 0.4% false acceptor site assignments. For the
large data set used in this study this means that on the average there
are one and a half false donor sites per true donor site and six false
acceptor sites per true acceptor site. With the joint assignment method
more than a fifth of the true donor sites and around one fourth of the
true acceptor sites could be detected without accompaniment of any
false positive predictions. Highly confident splice sites could not be
isolated with a widely used weight matrix method or by separate splice
site networks. A complementary relation between the confidence levels
of the coding/non-coding and the separate splice site networks was
observed, with many weak splice sites having sharp transitions in the
coding/non-coding signal and many stronger splice sites having more
ill-defined transitions between coding and non-coding.
Subject category: Genes, under the sub--headings: expression, sequence
and structure.
Keywords: Intron--splicing, human genes, exon selection, neural
network, computer--prediction.
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Hardcopies are also available:
S. Brunak and J. Engelbrecht
Department of Structural Properties of Materials
Building 307
The Technical University of Denmark
DK-2800 Lyngby, Denmark
brunak at nbivax.nbi.dk